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THE ROLE OF HUBNESS IN CLUSTERING HIGH-DIMENSIONAL DATA
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Description
High-dimensional data arise naturally in many domains, and have regularly presented a great challenge for traditional data-mining techniques, both in terms of effectiveness and efficiency.
We provide a new concept of using extended cluster feature vectors in order to make the algorithm scalable for very large databases
An objective function may be defined with respect to this distance function in order to measure the overall quality of a partition
It is possible for data to continue to be very sparse in all possible projected subsets of attributes of the original data.
